Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations3891
Missing cells27742
Missing cells (%)27.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 MiB
Average record size in memory882.0 B

Variable types

Text3
Categorical16
Numeric7

Alerts

cant_apercibimientos has constant value "1.0" Constant
cant_MontoLimite has constant value "1.0" Constant
cluster_k_4 has constant value "1" Constant
Estado is highly overall correlated with periodo_preinscripcionHigh correlation
TipoSocietario is highly overall correlated with periodo_preinscripcionHigh correlation
anio_preinscripcion is highly overall correlated with antiguedad and 1 other fieldsHigh correlation
antiguedad is highly overall correlated with anio_preinscripcion and 1 other fieldsHigh correlation
cant_Apoderado is highly overall correlated with cant_autenticado and 4 other fieldsHigh correlation
cant_antecedentes is highly overall correlated with cant_autenticado and 1 other fieldsHigh correlation
cant_autenticado is highly overall correlated with cant_Apoderado and 4 other fieldsHigh correlation
cant_noAutenticado is highly overall correlated with cant_Apoderado and 4 other fieldsHigh correlation
cant_procesos_adjudicado is highly overall correlated with cant_socios and 1 other fieldsHigh correlation
cant_representante is highly overall correlated with cant_sinMontoLimite and 4 other fieldsHigh correlation
cant_sinMontoLimite is highly overall correlated with cant_Apoderado and 4 other fieldsHigh correlation
cant_socios is highly overall correlated with cant_Apoderado and 8 other fieldsHigh correlation
cant_suspensiones is highly overall correlated with cant_antecedentes and 1 other fieldsHigh correlation
dmonto_total_adjudicado is highly overall correlated with cant_representante and 1 other fieldsHigh correlation
dtotal_articulos_provee is highly overall correlated with periodo_preinscripcionHigh correlation
monto_total_adjudicado is highly overall correlated with cant_noAutenticado and 2 other fieldsHigh correlation
periodo_preinscripcion is highly overall correlated with Estado and 11 other fieldsHigh correlation
provincia is highly overall correlated with cant_socios and 1 other fieldsHigh correlation
total_articulos_provee is highly overall correlated with cant_representante and 1 other fieldsHigh correlation
Estado is highly imbalanced (60.3%) Imbalance
TipoSocietario is highly imbalanced (88.3%) Imbalance
cant_Apoderado is highly imbalanced (84.7%) Imbalance
cant_representante is highly imbalanced (75.2%) Imbalance
cant_autenticado is highly imbalanced (85.9%) Imbalance
cant_noAutenticado is highly imbalanced (75.9%) Imbalance
cant_sinMontoLimite is highly imbalanced (82.4%) Imbalance
Estado has 98 (2.5%) missing values Missing
TipoSocietario has 98 (2.5%) missing values Missing
antiguedad has 98 (2.5%) missing values Missing
cant_socios has 3864 (99.3%) missing values Missing
cant_apercibimientos has 3867 (99.4%) missing values Missing
cant_suspensiones has 3858 (99.2%) missing values Missing
cant_antecedentes has 3841 (98.7%) missing values Missing
cant_Apoderado has 150 (3.9%) missing values Missing
cant_representante has 3786 (97.3%) missing values Missing
cant_autenticado has 98 (2.5%) missing values Missing
cant_noAutenticado has 3779 (97.1%) missing values Missing
cant_sinMontoLimite has 100 (2.6%) missing values Missing
cant_MontoLimite has 3885 (99.8%) missing values Missing
total_articulos_provee has 98 (2.5%) missing values Missing
dtotal_articulos_provee has 98 (2.5%) missing values Missing
monto_total_adjudicado is highly skewed (γ1 = 27.68665564) Skewed
CUIT has unique values Unique
periodo_preinscripcion has 98 (2.5%) zeros Zeros
antiguedad has 702 (18.0%) zeros Zeros

Reproduction

Analysis started2025-06-18 13:02:51.048779
Analysis finished2025-06-18 13:02:56.524171
Duration5.48 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

CUIT
Text

Unique 

Distinct3891
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size288.7 KiB
2025-06-18T10:02:56.694110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length11
Mean length10.966076
Min length3

Characters and Unicode

Total characters42669
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3891 ?
Unique (%)100.0%

Sample

1st row27236909900
2nd row20305924076
3rd row20082883240
4th row20230506060
5th row20141208269
ValueCountFrequency (%)
20303683489 1
 
< 0.1%
27331126530 1
 
< 0.1%
27236909900 1
 
< 0.1%
20305924076 1
 
< 0.1%
20082883240 1
 
< 0.1%
20230506060 1
 
< 0.1%
20141208269 1
 
< 0.1%
20271472901 1
 
< 0.1%
20302787221 1
 
< 0.1%
30546666707 1
 
< 0.1%
Other values (3881) 3881
99.7%
2025-06-18T10:02:56.969153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 8313
19.5%
0 6019
14.1%
3 4298
10.1%
1 4147
9.7%
7 4036
9.5%
4 3354
7.9%
9 3216
 
7.5%
6 3151
 
7.4%
8 3107
 
7.3%
5 3025
 
7.1%
Other values (2) 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42669
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 8313
19.5%
0 6019
14.1%
3 4298
10.1%
1 4147
9.7%
7 4036
9.5%
4 3354
7.9%
9 3216
 
7.5%
6 3151
 
7.4%
8 3107
 
7.3%
5 3025
 
7.1%
Other values (2) 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42669
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 8313
19.5%
0 6019
14.1%
3 4298
10.1%
1 4147
9.7%
7 4036
9.5%
4 3354
7.9%
9 3216
 
7.5%
6 3151
 
7.4%
8 3107
 
7.3%
5 3025
 
7.1%
Other values (2) 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42669
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 8313
19.5%
0 6019
14.1%
3 4298
10.1%
1 4147
9.7%
7 4036
9.5%
4 3354
7.9%
9 3216
 
7.5%
6 3151
 
7.4%
8 3107
 
7.3%
5 3025
 
7.1%
Other values (2) 3
 
< 0.1%

Nombre
Text

Distinct2873
Distinct (%)73.8%
Missing0
Missing (%)0.0%
Memory size319.4 KiB
2025-06-18T10:02:57.218051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length79
Median length57
Mean length16.074788
Min length1

Characters and Unicode

Total characters62547
Distinct characters91
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2869 ?
Unique (%)73.7%

Sample

1st rowEMR VENTAS & SERVICIOS
2nd rowSuministros EDA
3rd rowSEGUMAX de HORACIO MIGUEL ESPOSITO
4th rowsin datos
5th rowsin datos
ValueCountFrequency (%)
sin 1017
 
10.7%
datos 1016
 
10.6%
de 270
 
2.8%
servicios 103
 
1.1%
y 90
 
0.9%
la 75
 
0.8%
del 67
 
0.7%
municipalidad 62
 
0.6%
50
 
0.5%
daniel 45
 
0.5%
Other values (3667) 6747
70.7%
2025-06-18T10:02:57.545706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5651
 
9.0%
A 3923
 
6.3%
a 3312
 
5.3%
s 3110
 
5.0%
i 2930
 
4.7%
E 2869
 
4.6%
I 2791
 
4.5%
o 2657
 
4.2%
R 2418
 
3.9%
n 2366
 
3.8%
Other values (81) 30520
48.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 62547
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5651
 
9.0%
A 3923
 
6.3%
a 3312
 
5.3%
s 3110
 
5.0%
i 2930
 
4.7%
E 2869
 
4.6%
I 2791
 
4.5%
o 2657
 
4.2%
R 2418
 
3.9%
n 2366
 
3.8%
Other values (81) 30520
48.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 62547
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5651
 
9.0%
A 3923
 
6.3%
a 3312
 
5.3%
s 3110
 
5.0%
i 2930
 
4.7%
E 2869
 
4.6%
I 2791
 
4.5%
o 2657
 
4.2%
R 2418
 
3.9%
n 2366
 
3.8%
Other values (81) 30520
48.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 62547
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5651
 
9.0%
A 3923
 
6.3%
a 3312
 
5.3%
s 3110
 
5.0%
i 2930
 
4.7%
E 2869
 
4.6%
I 2791
 
4.5%
o 2657
 
4.2%
R 2418
 
3.9%
n 2366
 
3.8%
Other values (81) 30520
48.8%
Distinct1492
Distinct (%)38.3%
Missing0
Missing (%)0.0%
Memory size284.9 KiB
2025-06-18T10:02:57.732203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9748137
Min length9

Characters and Unicode

Total characters38812
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique613 ?
Unique (%)15.8%

Sample

1st row04/10/2016
2nd row13/10/2016
3rd row18/10/2016
4th row15/08/2016
5th row23/09/2016
ValueCountFrequency (%)
sin 98
 
2.5%
datos 98
 
2.5%
17/11/2021 25
 
0.6%
27/12/2016 13
 
0.3%
01/02/2017 13
 
0.3%
23/11/2016 12
 
0.3%
07/02/2017 11
 
0.3%
09/08/2017 11
 
0.3%
26/12/2016 11
 
0.3%
10/04/2017 11
 
0.3%
Other values (1483) 3686
92.4%
2025-06-18T10:02:58.036768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 8746
22.5%
/ 7586
19.5%
2 7301
18.8%
1 6421
16.5%
7 1912
 
4.9%
8 1418
 
3.7%
9 1112
 
2.9%
6 1094
 
2.8%
3 917
 
2.4%
5 744
 
1.9%
Other values (9) 1561
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 38812
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 8746
22.5%
/ 7586
19.5%
2 7301
18.8%
1 6421
16.5%
7 1912
 
4.9%
8 1418
 
3.7%
9 1112
 
2.9%
6 1094
 
2.8%
3 917
 
2.4%
5 744
 
1.9%
Other values (9) 1561
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 38812
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 8746
22.5%
/ 7586
19.5%
2 7301
18.8%
1 6421
16.5%
7 1912
 
4.9%
8 1418
 
3.7%
9 1112
 
2.9%
6 1094
 
2.8%
3 917
 
2.4%
5 744
 
1.9%
Other values (9) 1561
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 38812
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 8746
22.5%
/ 7586
19.5%
2 7301
18.8%
1 6421
16.5%
7 1912
 
4.9%
8 1418
 
3.7%
9 1112
 
2.9%
6 1094
 
2.8%
3 917
 
2.4%
5 744
 
1.9%
Other values (9) 1561
 
4.0%

Estado
Categorical

High correlation  Imbalance  Missing 

Distinct8
Distinct (%)0.2%
Missing98
Missing (%)2.5%
Memory size292.6 KiB
Inscripto
2919 
Pre Inscripto
446 
Desactualizado Por Documentos Vencidos
 
272
Con Solicitud De Baja
 
55
Desactualizado Por Mantencion Formulario
 
54
Other values (3)
 
47

Length

Max length40
Median length9
Mean length12.330082
Min length9

Characters and Unicode

Total characters46768
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowInscripto
2nd rowInscripto
3rd rowDesactualizado Por Documentos Vencidos
4th rowInscripto
5th rowInscripto

Common Values

ValueCountFrequency (%)
Inscripto 2919
75.0%
Pre Inscripto 446
 
11.5%
Desactualizado Por Documentos Vencidos 272
 
7.0%
Con Solicitud De Baja 55
 
1.4%
Desactualizado Por Mantencion Formulario 54
 
1.4%
Desactualizado Por Clase 40
 
1.0%
En Evaluacion 6
 
0.2%
Suspendido 1
 
< 0.1%
(Missing) 98
 
2.5%

Length

2025-06-18T10:02:58.127389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:02:58.242093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
inscripto 3365
61.5%
pre 446
 
8.2%
desactualizado 366
 
6.7%
por 366
 
6.7%
documentos 272
 
5.0%
vencidos 272
 
5.0%
con 55
 
1.0%
solicitud 55
 
1.0%
de 55
 
1.0%
baja 55
 
1.0%
Other values (6) 161
 
2.9%

Most occurring characters

ValueCountFrequency (%)
o 5192
11.1%
c 4390
9.4%
s 4316
9.2%
r 4285
9.2%
i 4228
9.0%
n 4139
8.9%
t 4112
8.8%
p 3366
7.2%
I 3365
7.2%
1675
 
3.6%
Other values (18) 7700
16.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46768
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 5192
11.1%
c 4390
9.4%
s 4316
9.2%
r 4285
9.2%
i 4228
9.0%
n 4139
8.9%
t 4112
8.8%
p 3366
7.2%
I 3365
7.2%
1675
 
3.6%
Other values (18) 7700
16.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46768
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 5192
11.1%
c 4390
9.4%
s 4316
9.2%
r 4285
9.2%
i 4228
9.0%
n 4139
8.9%
t 4112
8.8%
p 3366
7.2%
I 3365
7.2%
1675
 
3.6%
Other values (18) 7700
16.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46768
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 5192
11.1%
c 4390
9.4%
s 4316
9.2%
r 4285
9.2%
i 4228
9.0%
n 4139
8.9%
t 4112
8.8%
p 3366
7.2%
I 3365
7.2%
1675
 
3.6%
Other values (18) 7700
16.5%

TipoSocietario
Categorical

High correlation  Imbalance  Missing 

Distinct6
Distinct (%)0.2%
Missing98
Missing (%)2.5%
Memory size441.6 KiB
Persona Física
3622 
Organismo Publico
 
144
Sociedad Anónima
 
21
Otras Formas Societarias
 
2
Sociedades De Hecho
 
2

Length

Max length24
Median length14
Mean length14.131822
Min length12

Characters and Unicode

Total characters53602
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPersona Física
2nd rowPersona Física
3rd rowPersona Física
4th rowPersona Física
5th rowPersona Física

Common Values

ValueCountFrequency (%)
Persona Física 3622
93.1%
Organismo Publico 144
 
3.7%
Sociedad Anónima 21
 
0.5%
Otras Formas Societarias 2
 
0.1%
Sociedades De Hecho 2
 
0.1%
Cooperativas 2
 
0.1%
(Missing) 98
 
2.5%

Length

2025-06-18T10:02:58.375103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:02:58.439772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
persona 3622
47.7%
física 3622
47.7%
organismo 144
 
1.9%
publico 144
 
1.9%
sociedad 21
 
0.3%
anónima 21
 
0.3%
otras 2
 
< 0.1%
formas 2
 
< 0.1%
societarias 2
 
< 0.1%
sociedades 2
 
< 0.1%
Other values (3) 6
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 7444
13.9%
s 7398
13.8%
i 3960
7.4%
o 3943
7.4%
n 3808
7.1%
3795
7.1%
c 3793
7.1%
r 3774
7.0%
P 3766
7.0%
e 3655
6.8%
Other values (19) 8266
15.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 53602
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 7444
13.9%
s 7398
13.8%
i 3960
7.4%
o 3943
7.4%
n 3808
7.1%
3795
7.1%
c 3793
7.1%
r 3774
7.0%
P 3766
7.0%
e 3655
6.8%
Other values (19) 8266
15.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 53602
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 7444
13.9%
s 7398
13.8%
i 3960
7.4%
o 3943
7.4%
n 3808
7.1%
3795
7.1%
c 3793
7.1%
r 3774
7.0%
P 3766
7.0%
e 3655
6.8%
Other values (19) 8266
15.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 53602
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 7444
13.9%
s 7398
13.8%
i 3960
7.4%
o 3943
7.4%
n 3808
7.1%
3795
7.1%
c 3793
7.1%
r 3774
7.0%
P 3766
7.0%
e 3655
6.8%
Other values (19) 8266
15.4%

periodo_preinscripcion
Real number (ℝ)

High correlation  Zeros 

Distinct79
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean196767.38
Minimum0
Maximum202303
Zeros98
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size60.8 KiB
2025-06-18T10:02:58.525221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile201610
Q1201705
median201805
Q3202005
95-th percentile202203
Maximum202303
Range202303
Interquartile range (IQR)300

Descriptive statistics

Standard deviation31632.764
Coefficient of variation (CV)0.16076224
Kurtosis34.773837
Mean196767.38
Median Absolute Deviation (MAD)102
Skewness-6.0625672
Sum7.6562186 × 108
Variance1.0006317 × 109
MonotonicityNot monotonic
2025-06-18T10:02:58.625257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201705 128
 
3.3%
201611 126
 
3.2%
201706 120
 
3.1%
201703 114
 
2.9%
201612 113
 
2.9%
201704 109
 
2.8%
201702 108
 
2.8%
201708 107
 
2.7%
201701 107
 
2.7%
0 98
 
2.5%
Other values (69) 2761
71.0%
ValueCountFrequency (%)
0 98
2.5%
201607 11
 
0.3%
201608 24
 
0.6%
201609 23
 
0.6%
201610 51
1.3%
201611 126
3.2%
201612 113
2.9%
201701 107
2.7%
201702 108
2.8%
201703 114
2.9%
ValueCountFrequency (%)
202303 1
 
< 0.1%
202211 6
 
0.2%
202210 10
 
0.3%
202209 28
0.7%
202208 32
0.8%
202207 19
0.5%
202206 26
0.7%
202205 34
0.9%
202204 31
0.8%
202203 29
0.7%

anio_preinscripcion
Categorical

High correlation 

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size262.7 KiB
2017
1189 
2018
711 
2019
468 
2021
443 
2020
375 
Other values (4)
705 

Length

Max length9
Median length4
Mean length4.1259316
Min length4

Characters and Unicode

Total characters16054
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2017 1189
30.6%
2018 711
18.3%
2019 468
 
12.0%
2021 443
 
11.4%
2020 375
 
9.6%
2016 348
 
8.9%
2022 258
 
6.6%
sin datos 98
 
2.5%
2023 1
 
< 0.1%

Length

2025-06-18T10:02:58.718028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:02:58.796690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2017 1189
29.8%
2018 711
17.8%
2019 468
 
11.7%
2021 443
 
11.1%
2020 375
 
9.4%
2016 348
 
8.7%
2022 258
 
6.5%
sin 98
 
2.5%
datos 98
 
2.5%
2023 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2 5128
31.9%
0 4168
26.0%
1 3159
19.7%
7 1189
 
7.4%
8 711
 
4.4%
9 468
 
2.9%
6 348
 
2.2%
s 196
 
1.2%
i 98
 
0.6%
n 98
 
0.6%
Other values (6) 491
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16054
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 5128
31.9%
0 4168
26.0%
1 3159
19.7%
7 1189
 
7.4%
8 711
 
4.4%
9 468
 
2.9%
6 348
 
2.2%
s 196
 
1.2%
i 98
 
0.6%
n 98
 
0.6%
Other values (6) 491
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16054
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 5128
31.9%
0 4168
26.0%
1 3159
19.7%
7 1189
 
7.4%
8 711
 
4.4%
9 468
 
2.9%
6 348
 
2.2%
s 196
 
1.2%
i 98
 
0.6%
n 98
 
0.6%
Other values (6) 491
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16054
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 5128
31.9%
0 4168
26.0%
1 3159
19.7%
7 1189
 
7.4%
8 711
 
4.4%
9 468
 
2.9%
6 348
 
2.2%
s 196
 
1.2%
i 98
 
0.6%
n 98
 
0.6%
Other values (6) 491
 
3.1%

cant_procesos_adjudicado
Real number (ℝ)

High correlation 

Distinct138
Distinct (%)3.6%
Missing6
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean9.1660232
Minimum1
Maximum895
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.8 KiB
2025-06-18T10:02:58.906052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile31
Maximum895
Range894
Interquartile range (IQR)3

Descriptive statistics

Standard deviation41.114995
Coefficient of variation (CV)4.4855871
Kurtosis217.13381
Mean9.1660232
Median Absolute Deviation (MAD)1
Skewness12.885379
Sum35610
Variance1690.4428
MonotonicityNot monotonic
2025-06-18T10:02:59.018150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1728
44.4%
2 715
18.4%
3 366
 
9.4%
4 209
 
5.4%
5 126
 
3.2%
6 102
 
2.6%
7 63
 
1.6%
8 47
 
1.2%
9 43
 
1.1%
11 40
 
1.0%
Other values (128) 446
 
11.5%
ValueCountFrequency (%)
1 1728
44.4%
2 715
18.4%
3 366
 
9.4%
4 209
 
5.4%
5 126
 
3.2%
6 102
 
2.6%
7 63
 
1.6%
8 47
 
1.2%
9 43
 
1.1%
10 32
 
0.8%
ValueCountFrequency (%)
895 1
< 0.1%
889 1
< 0.1%
804 1
< 0.1%
792 1
< 0.1%
635 1
< 0.1%
525 1
< 0.1%
438 1
< 0.1%
409 1
< 0.1%
382 1
< 0.1%
379 1
< 0.1%

monto_total_adjudicado
Real number (ℝ)

High correlation  Skewed 

Distinct3715
Distinct (%)95.6%
Missing6
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean15961656
Minimum0
Maximum5.0920925 × 109
Zeros35
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size60.8 KiB
2025-06-18T10:02:59.111891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34459.459
Q1376952.96
median1645642.5
Q36710526.3
95-th percentile50904056
Maximum5.0920925 × 109
Range5.0920925 × 109
Interquartile range (IQR)6333573.4

Descriptive statistics

Standard deviation1.2177206 × 108
Coefficient of variation (CV)7.6290368
Kurtosis966.17605
Mean15961656
Median Absolute Deviation (MAD)1518142.5
Skewness27.686656
Sum6.2011034 × 1010
Variance1.4828435 × 1016
MonotonicityNot monotonic
2025-06-18T10:02:59.205702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 35
 
0.9%
6710526.316 7
 
0.2%
85000 4
 
0.1%
40800 4
 
0.1%
114750 4
 
0.1%
526451.6129 4
 
0.1%
240428.5714 4
 
0.1%
255000 4
 
0.1%
21857.14286 4
 
0.1%
49038.46154 3
 
0.1%
Other values (3705) 3812
98.0%
(Missing) 6
 
0.2%
ValueCountFrequency (%)
0 35
0.9%
0.010851064 1
 
< 0.1%
0.056666667 1
 
< 0.1%
0.80952381 1
 
< 0.1%
1.65952381 1
 
< 0.1%
2.487804878 2
 
0.1%
12.648 1
 
< 0.1%
36.13714286 1
 
< 0.1%
120.2142857 1
 
< 0.1%
154.1027027 1
 
< 0.1%
ValueCountFrequency (%)
5092092510 1
< 0.1%
3205078998 1
< 0.1%
2525171710 1
< 0.1%
1624050032 1
< 0.1%
1501157408 1
< 0.1%
1386549515 1
< 0.1%
1129301338 1
< 0.1%
1023647402 1
< 0.1%
1006323598 1
< 0.1%
778852664.9 1
< 0.1%

antiguedad
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct6
Distinct (%)0.2%
Missing98
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean2.6206169
Minimum0
Maximum5
Zeros702
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size60.8 KiB
2025-06-18T10:02:59.273198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.649788
Coefficient of variation (CV)0.62954185
Kurtosis-1.1755102
Mean2.6206169
Median Absolute Deviation (MAD)1
Skewness-0.38163944
Sum9940
Variance2.7218006
MonotonicityNot monotonic
2025-06-18T10:02:59.324466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 1189
30.6%
3 711
18.3%
0 702
18.0%
2 468
 
12.0%
1 375
 
9.6%
5 348
 
8.9%
(Missing) 98
 
2.5%
ValueCountFrequency (%)
0 702
18.0%
1 375
 
9.6%
2 468
 
12.0%
3 711
18.3%
4 1189
30.6%
5 348
 
8.9%
ValueCountFrequency (%)
5 348
 
8.9%
4 1189
30.6%
3 711
18.3%
2 468
 
12.0%
1 375
 
9.6%
0 702
18.0%

provincia
Categorical

High correlation 

Distinct27
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size357.8 KiB
Buenos Aires
1139 
Ciudad Autónoma de Buenos Aires
809 
Córdoba
255 
Santa Fe
142 
Chubut
 
133
Other values (22)
1413 

Length

Max length31
Median length19
Mean length14.013878
Min length1

Characters and Unicode

Total characters54528
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowChubut
2nd rowCiudad Autónoma de Buenos Aires
3rd rowCiudad Autónoma de Buenos Aires
4th rowCiudad Autónoma de Buenos Aires
5th rowCiudad Autónoma de Buenos Aires

Common Values

ValueCountFrequency (%)
Buenos Aires 1139
29.3%
Ciudad Autónoma de Buenos Aires 809
20.8%
Córdoba 255
 
6.6%
Santa Fe 142
 
3.6%
Chubut 133
 
3.4%
Mendoza 128
 
3.3%
sin datos 115
 
3.0%
Misiones 112
 
2.9%
Rio Negro 111
 
2.9%
Entre Rios 91
 
2.3%
Other values (17) 856
22.0%

Length

2025-06-18T10:02:59.402062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
buenos 1948
21.2%
aires 1948
21.2%
ciudad 809
8.8%
autónoma 809
8.8%
de 809
8.8%
córdoba 255
 
2.8%
santa 210
 
2.3%
fe 142
 
1.5%
chubut 133
 
1.4%
mendoza 128
 
1.4%
Other values (28) 1990
21.7%

Most occurring characters

ValueCountFrequency (%)
e 5776
10.6%
5290
 
9.7%
s 4656
 
8.5%
u 4410
 
8.1%
o 4147
 
7.6%
n 3784
 
6.9%
i 3545
 
6.5%
a 3486
 
6.4%
d 3033
 
5.6%
r 2877
 
5.3%
Other values (30) 13524
24.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54528
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 5776
10.6%
5290
 
9.7%
s 4656
 
8.5%
u 4410
 
8.1%
o 4147
 
7.6%
n 3784
 
6.9%
i 3545
 
6.5%
a 3486
 
6.4%
d 3033
 
5.6%
r 2877
 
5.3%
Other values (30) 13524
24.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54528
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 5776
10.6%
5290
 
9.7%
s 4656
 
8.5%
u 4410
 
8.1%
o 4147
 
7.6%
n 3784
 
6.9%
i 3545
 
6.5%
a 3486
 
6.4%
d 3033
 
5.6%
r 2877
 
5.3%
Other values (30) 13524
24.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54528
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 5776
10.6%
5290
 
9.7%
s 4656
 
8.5%
u 4410
 
8.1%
o 4147
 
7.6%
n 3784
 
6.9%
i 3545
 
6.5%
a 3486
 
6.4%
d 3033
 
5.6%
r 2877
 
5.3%
Other values (30) 13524
24.8%

cant_socios
Categorical

High correlation  Missing 

Distinct5
Distinct (%)18.5%
Missing3864
Missing (%)99.3%
Memory size243.3 KiB
1.0
12 
2.0
10 
3.0
4.0
16.0
 
1

Length

Max length4
Median length3
Mean length3.037037
Min length3

Characters and Unicode

Total characters82
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.7%

Sample

1st row2.0
2nd row2.0
3rd row16.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 12
 
0.3%
2.0 10
 
0.3%
3.0 2
 
0.1%
4.0 2
 
0.1%
16.0 1
 
< 0.1%
(Missing) 3864
99.3%

Length

2025-06-18T10:02:59.475886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:02:59.525496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 12
44.4%
2.0 10
37.0%
3.0 2
 
7.4%
4.0 2
 
7.4%
16.0 1
 
3.7%

Most occurring characters

ValueCountFrequency (%)
. 27
32.9%
0 27
32.9%
1 13
15.9%
2 10
 
12.2%
3 2
 
2.4%
4 2
 
2.4%
6 1
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 82
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 27
32.9%
0 27
32.9%
1 13
15.9%
2 10
 
12.2%
3 2
 
2.4%
4 2
 
2.4%
6 1
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 82
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 27
32.9%
0 27
32.9%
1 13
15.9%
2 10
 
12.2%
3 2
 
2.4%
4 2
 
2.4%
6 1
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 82
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 27
32.9%
0 27
32.9%
1 13
15.9%
2 10
 
12.2%
3 2
 
2.4%
4 2
 
2.4%
6 1
 
1.2%

cant_apercibimientos
Categorical

Constant  Missing 

Distinct1
Distinct (%)4.2%
Missing3867
Missing (%)99.4%
Memory size243.3 KiB
1.0
24 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters72
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 24
 
0.6%
(Missing) 3867
99.4%

Length

2025-06-18T10:02:59.593074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:02:59.618052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 24
100.0%

Most occurring characters

ValueCountFrequency (%)
1 24
33.3%
. 24
33.3%
0 24
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 24
33.3%
. 24
33.3%
0 24
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 24
33.3%
. 24
33.3%
0 24
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 24
33.3%
. 24
33.3%
0 24
33.3%

cant_suspensiones
Real number (ℝ)

High correlation  Missing 

Distinct6
Distinct (%)18.2%
Missing3858
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean2.2121212
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.8 KiB
2025-06-18T10:02:59.649378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5361798
Coefficient of variation (CV)0.69443746
Kurtosis3.3438087
Mean2.2121212
Median Absolute Deviation (MAD)1
Skewness1.9328412
Sum73
Variance2.3598485
MonotonicityNot monotonic
2025-06-18T10:02:59.711792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 16
 
0.4%
1 11
 
0.3%
6 2
 
0.1%
4 2
 
0.1%
3 1
 
< 0.1%
7 1
 
< 0.1%
(Missing) 3858
99.2%
ValueCountFrequency (%)
1 11
0.3%
2 16
0.4%
3 1
 
< 0.1%
4 2
 
0.1%
6 2
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 2
 
0.1%
4 2
 
0.1%
3 1
 
< 0.1%
2 16
0.4%
1 11
0.3%

cant_antecedentes
Real number (ℝ)

High correlation  Missing 

Distinct8
Distinct (%)16.0%
Missing3841
Missing (%)98.7%
Infinite0
Infinite (%)0.0%
Mean2.08
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.8 KiB
2025-06-18T10:02:59.759235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile5.55
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5231546
Coefficient of variation (CV)0.73228588
Kurtosis6.1363264
Mean2.08
Median Absolute Deviation (MAD)1
Skewness2.4225506
Sum104
Variance2.32
MonotonicityNot monotonic
2025-06-18T10:02:59.821729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 24
 
0.6%
1 19
 
0.5%
4 2
 
0.1%
7 1
 
< 0.1%
5 1
 
< 0.1%
3 1
 
< 0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
(Missing) 3841
98.7%
ValueCountFrequency (%)
1 19
0.5%
2 24
0.6%
3 1
 
< 0.1%
4 2
 
0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 1
 
< 0.1%
6 1
 
< 0.1%
5 1
 
< 0.1%
4 2
 
0.1%
3 1
 
< 0.1%
2 24
0.6%
1 19
0.5%

cant_Apoderado
Categorical

High correlation  Imbalance  Missing 

Distinct5
Distinct (%)0.1%
Missing150
Missing (%)3.9%
Memory size257.8 KiB
1.0
3516 
2.0
 
208
3.0
 
14
4.0
 
2
10.0
 
1

Length

Max length4
Median length3
Mean length3.0002673
Min length3

Characters and Unicode

Total characters11224
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 3516
90.4%
2.0 208
 
5.3%
3.0 14
 
0.4%
4.0 2
 
0.1%
10.0 1
 
< 0.1%
(Missing) 150
 
3.9%

Length

2025-06-18T10:02:59.891218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:02:59.935432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3516
94.0%
2.0 208
 
5.6%
3.0 14
 
0.4%
4.0 2
 
0.1%
10.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 3742
33.3%
. 3741
33.3%
1 3517
31.3%
2 208
 
1.9%
3 14
 
0.1%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11224
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3742
33.3%
. 3741
33.3%
1 3517
31.3%
2 208
 
1.9%
3 14
 
0.1%
4 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11224
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3742
33.3%
. 3741
33.3%
1 3517
31.3%
2 208
 
1.9%
3 14
 
0.1%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11224
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3742
33.3%
. 3741
33.3%
1 3517
31.3%
2 208
 
1.9%
3 14
 
0.1%
4 2
 
< 0.1%

cant_representante
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)2.9%
Missing3786
Missing (%)97.3%
Memory size243.6 KiB
1.0
98 
2.0
 
6
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters315
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 98
 
2.5%
2.0 6
 
0.2%
4.0 1
 
< 0.1%
(Missing) 3786
97.3%

Length

2025-06-18T10:03:00.004742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:03:00.056504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 98
93.3%
2.0 6
 
5.7%
4.0 1
 
1.0%

Most occurring characters

ValueCountFrequency (%)
. 105
33.3%
0 105
33.3%
1 98
31.1%
2 6
 
1.9%
4 1
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 315
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 105
33.3%
0 105
33.3%
1 98
31.1%
2 6
 
1.9%
4 1
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 315
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 105
33.3%
0 105
33.3%
1 98
31.1%
2 6
 
1.9%
4 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 315
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 105
33.3%
0 105
33.3%
1 98
31.1%
2 6
 
1.9%
4 1
 
0.3%

cant_autenticado
Categorical

High correlation  Imbalance  Missing 

Distinct4
Distinct (%)0.1%
Missing98
Missing (%)2.5%
Memory size258.0 KiB
1.0
3622 
2.0
 
159
3.0
 
11
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11379
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 3622
93.1%
2.0 159
 
4.1%
3.0 11
 
0.3%
4.0 1
 
< 0.1%
(Missing) 98
 
2.5%

Length

2025-06-18T10:03:00.107889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:03:00.161748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3622
95.5%
2.0 159
 
4.2%
3.0 11
 
0.3%
4.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 3793
33.3%
0 3793
33.3%
1 3622
31.8%
2 159
 
1.4%
3 11
 
0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 3793
33.3%
0 3793
33.3%
1 3622
31.8%
2 159
 
1.4%
3 11
 
0.1%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 3793
33.3%
0 3793
33.3%
1 3622
31.8%
2 159
 
1.4%
3 11
 
0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 3793
33.3%
0 3793
33.3%
1 3622
31.8%
2 159
 
1.4%
3 11
 
0.1%
4 1
 
< 0.1%

cant_noAutenticado
Categorical

High correlation  Imbalance  Missing 

Distinct4
Distinct (%)3.6%
Missing3779
Missing (%)97.1%
Memory size243.6 KiB
1.0
103 
2.0
 
7
3.0
 
1
9.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters336
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.8%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 103
 
2.6%
2.0 7
 
0.2%
3.0 1
 
< 0.1%
9.0 1
 
< 0.1%
(Missing) 3779
97.1%

Length

2025-06-18T10:03:00.216096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:03:00.247344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 103
92.0%
2.0 7
 
6.2%
3.0 1
 
0.9%
9.0 1
 
0.9%

Most occurring characters

ValueCountFrequency (%)
. 112
33.3%
0 112
33.3%
1 103
30.7%
2 7
 
2.1%
3 1
 
0.3%
9 1
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 336
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 112
33.3%
0 112
33.3%
1 103
30.7%
2 7
 
2.1%
3 1
 
0.3%
9 1
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 336
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 112
33.3%
0 112
33.3%
1 103
30.7%
2 7
 
2.1%
3 1
 
0.3%
9 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 336
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 112
33.3%
0 112
33.3%
1 103
30.7%
2 7
 
2.1%
3 1
 
0.3%
9 1
 
0.3%

cant_sinMontoLimite
Categorical

High correlation  Imbalance  Missing 

Distinct5
Distinct (%)0.1%
Missing100
Missing (%)2.6%
Memory size258.0 KiB
1.0
3517 
2.0
 
251
3.0
 
19
4.0
 
3
12.0
 
1

Length

Max length4
Median length3
Mean length3.0002638
Min length3

Characters and Unicode

Total characters11374
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 3517
90.4%
2.0 251
 
6.5%
3.0 19
 
0.5%
4.0 3
 
0.1%
12.0 1
 
< 0.1%
(Missing) 100
 
2.6%

Length

2025-06-18T10:03:00.319328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:03:00.366268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3517
92.8%
2.0 251
 
6.6%
3.0 19
 
0.5%
4.0 3
 
0.1%
12.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 3791
33.3%
0 3791
33.3%
1 3518
30.9%
2 252
 
2.2%
3 19
 
0.2%
4 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11374
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 3791
33.3%
0 3791
33.3%
1 3518
30.9%
2 252
 
2.2%
3 19
 
0.2%
4 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11374
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 3791
33.3%
0 3791
33.3%
1 3518
30.9%
2 252
 
2.2%
3 19
 
0.2%
4 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11374
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 3791
33.3%
0 3791
33.3%
1 3518
30.9%
2 252
 
2.2%
3 19
 
0.2%
4 3
 
< 0.1%

cant_MontoLimite
Categorical

Constant  Missing 

Distinct1
Distinct (%)16.7%
Missing3885
Missing (%)99.8%
Memory size243.2 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 6
 
0.2%
(Missing) 3885
99.8%

Length

2025-06-18T10:03:00.437464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:03:00.471148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 6
100.0%

Most occurring characters

ValueCountFrequency (%)
1 6
33.3%
. 6
33.3%
0 6
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 6
33.3%
. 6
33.3%
0 6
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 6
33.3%
. 6
33.3%
0 6
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 6
33.3%
. 6
33.3%
0 6
33.3%

total_articulos_provee
Real number (ℝ)

High correlation  Missing 

Distinct477
Distinct (%)12.6%
Missing98
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean78.913525
Minimum1
Maximum6993
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.8 KiB
2025-06-18T10:03:00.541543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median5
Q337
95-th percentile437.8
Maximum6993
Range6992
Interquartile range (IQR)36

Descriptive statistics

Standard deviation287.25365
Coefficient of variation (CV)3.6401067
Kurtosis245.11641
Mean78.913525
Median Absolute Deviation (MAD)4
Skewness12.889009
Sum299319
Variance82514.66
MonotonicityNot monotonic
2025-06-18T10:03:00.636502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1266
32.5%
2 302
 
7.8%
3 180
 
4.6%
4 125
 
3.2%
5 122
 
3.1%
6 86
 
2.2%
7 65
 
1.7%
8 64
 
1.6%
9 56
 
1.4%
11 48
 
1.2%
Other values (467) 1479
38.0%
(Missing) 98
 
2.5%
ValueCountFrequency (%)
1 1266
32.5%
2 302
 
7.8%
3 180
 
4.6%
4 125
 
3.2%
5 122
 
3.1%
6 86
 
2.2%
7 65
 
1.7%
8 64
 
1.6%
9 56
 
1.4%
10 40
 
1.0%
ValueCountFrequency (%)
6993 1
< 0.1%
6661 1
< 0.1%
5612 1
< 0.1%
4867 1
< 0.1%
4471 1
< 0.1%
3956 1
< 0.1%
2868 1
< 0.1%
1922 1
< 0.1%
1742 1
< 0.1%
1732 1
< 0.1%

dmonto_total_adjudicado
Categorical

High correlation 

Distinct20
Distinct (%)0.5%
Missing6
Missing (%)0.2%
Memory size339.1 KiB
(1302657.558, 1793326.755]
282 
(377939.298, 599760.0]
268 
(104767.373, 224078.198]
268 
(33011.111, 104767.373]
261 
(224078.198, 377939.298]
258 
Other values (15)
2548 

Length

Max length29
Median length28
Mean length24.267439
Min length19

Characters and Unicode

Total characters94279
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(9424898.401, 13557176.81]
2nd row(89439449.702, 222964579.98]
3rd row(890758.9, 1302657.558]
4th row(89439449.702, 222964579.98]
5th row(599760.0, 890758.9]

Common Values

ValueCountFrequency (%)
(1302657.558, 1793326.755] 282
 
7.2%
(377939.298, 599760.0] 268
 
6.9%
(104767.373, 224078.198] 268
 
6.9%
(33011.111, 104767.373] 261
 
6.7%
(224078.198, 377939.298] 258
 
6.6%
(890758.9, 1302657.558] 249
 
6.4%
(1793326.755, 2483085.385] 242
 
6.2%
(599760.0, 890758.9] 240
 
6.2%
(2483085.385, 3396600.0] 238
 
6.1%
(3396600.0, 4727330.113] 206
 
5.3%
Other values (10) 1373
35.3%

Length

2025-06-18T10:03:00.716795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1302657.558 531
 
6.8%
104767.373 529
 
6.8%
377939.298 526
 
6.8%
224078.198 526
 
6.8%
1793326.755 524
 
6.7%
599760.0 508
 
6.5%
890758.9 489
 
6.3%
2483085.385 480
 
6.2%
33011.111 447
 
5.8%
3396600.0 444
 
5.7%
Other values (11) 2766
35.6%

Most occurring characters

ValueCountFrequency (%)
7 9219
9.8%
3 8706
9.2%
1 8284
8.8%
9 7933
 
8.4%
. 7770
 
8.2%
0 7587
 
8.0%
8 7585
 
8.0%
5 7027
 
7.5%
2 5152
 
5.5%
6 4909
 
5.2%
Other values (6) 20107
21.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 94279
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 9219
9.8%
3 8706
9.2%
1 8284
8.8%
9 7933
 
8.4%
. 7770
 
8.2%
0 7587
 
8.0%
8 7585
 
8.0%
5 7027
 
7.5%
2 5152
 
5.5%
6 4909
 
5.2%
Other values (6) 20107
21.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 94279
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 9219
9.8%
3 8706
9.2%
1 8284
8.8%
9 7933
 
8.4%
. 7770
 
8.2%
0 7587
 
8.0%
8 7585
 
8.0%
5 7027
 
7.5%
2 5152
 
5.5%
6 4909
 
5.2%
Other values (6) 20107
21.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 94279
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 9219
9.8%
3 8706
9.2%
1 8284
8.8%
9 7933
 
8.4%
. 7770
 
8.2%
0 7587
 
8.0%
8 7585
 
8.0%
5 7027
 
7.5%
2 5152
 
5.5%
6 4909
 
5.2%
Other values (6) 20107
21.3%
Distinct10
Distinct (%)0.3%
Missing6
Missing (%)0.2%
Memory size290.9 KiB
(0.999, 2.0]
2443 
(2.0, 3.0]
366 
(3.0, 4.0]
 
209
(39.0, 1214.0]
 
157
(8.0, 12.0]
 
139
Other values (5)
571 

Length

Max length14
Median length12
Mean length11.575032
Min length10

Characters and Unicode

Total characters44969
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(39.0, 1214.0]
2nd row(39.0, 1214.0]
3rd row(0.999, 2.0]
4th row(8.0, 12.0]
5th row(0.999, 2.0]

Common Values

ValueCountFrequency (%)
(0.999, 2.0] 2443
62.8%
(2.0, 3.0] 366
 
9.4%
(3.0, 4.0] 209
 
5.4%
(39.0, 1214.0] 157
 
4.0%
(8.0, 12.0] 139
 
3.6%
(4.0, 5.0] 126
 
3.2%
(19.0, 39.0] 120
 
3.1%
(12.0, 19.0] 113
 
2.9%
(6.0, 8.0] 110
 
2.8%
(5.0, 6.0] 102
 
2.6%
(Missing) 6
 
0.2%

Length

2025-06-18T10:03:00.796684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:03:00.869480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 2809
36.2%
0.999 2443
31.4%
3.0 575
 
7.4%
4.0 335
 
4.3%
39.0 277
 
3.6%
12.0 252
 
3.2%
8.0 249
 
3.2%
19.0 233
 
3.0%
5.0 228
 
2.9%
6.0 212
 
2.7%

Most occurring characters

ValueCountFrequency (%)
9 7839
17.4%
0 7770
17.3%
. 7770
17.3%
( 3885
8.6%
, 3885
8.6%
3885
8.6%
] 3885
8.6%
2 3218
7.2%
3 852
 
1.9%
1 799
 
1.8%
Other values (4) 1181
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 44969
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 7839
17.4%
0 7770
17.3%
. 7770
17.3%
( 3885
8.6%
, 3885
8.6%
3885
8.6%
] 3885
8.6%
2 3218
7.2%
3 852
 
1.9%
1 799
 
1.8%
Other values (4) 1181
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 44969
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 7839
17.4%
0 7770
17.3%
. 7770
17.3%
( 3885
8.6%
, 3885
8.6%
3885
8.6%
] 3885
8.6%
2 3218
7.2%
3 852
 
1.9%
1 799
 
1.8%
Other values (4) 1181
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 44969
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 7839
17.4%
0 7770
17.3%
. 7770
17.3%
( 3885
8.6%
, 3885
8.6%
3885
8.6%
] 3885
8.6%
2 3218
7.2%
3 852
 
1.9%
1 799
 
1.8%
Other values (4) 1181
 
2.6%

dtotal_articulos_provee
Categorical

High correlation  Missing 

Distinct15
Distinct (%)0.4%
Missing98
Missing (%)2.5%
Memory size291.2 KiB
(0.999, 2.0]
1568 
(345.0, 6993.0]
243 
(4.0, 6.0]
208 
(161.0, 345.0]
187 
(2.0, 3.0]
180 
Other values (10)
1407 

Length

Max length15
Median length12
Mean length11.96019
Min length10

Characters and Unicode

Total characters45365
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(58.0, 97.6]
2nd row(97.6, 161.0]
3rd row(161.0, 345.0]
4th row(4.0, 6.0]
5th row(15.0, 21.0]

Common Values

ValueCountFrequency (%)
(0.999, 2.0] 1568
40.3%
(345.0, 6993.0] 243
 
6.2%
(4.0, 6.0] 208
 
5.3%
(161.0, 345.0] 187
 
4.8%
(2.0, 3.0] 180
 
4.6%
(97.6, 161.0] 174
 
4.5%
(58.0, 97.6] 154
 
4.0%
(8.0, 11.0] 144
 
3.7%
(40.0, 58.0] 144
 
3.7%
(15.0, 21.0] 139
 
3.6%
Other values (5) 652
16.8%

Length

2025-06-18T10:03:00.951986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2.0 1748
23.0%
0.999 1568
20.7%
345.0 430
 
5.7%
161.0 361
 
4.8%
6.0 337
 
4.4%
4.0 333
 
4.4%
97.6 328
 
4.3%
3.0 305
 
4.0%
58.0 298
 
3.9%
40.0 278
 
3.7%
Other values (6) 1600
21.1%

Most occurring characters

ValueCountFrequency (%)
. 7586
16.7%
0 7536
16.6%
9 5786
12.8%
( 3793
8.4%
, 3793
8.4%
3793
8.4%
] 3793
8.4%
2 2289
 
5.0%
1 1812
 
4.0%
6 1269
 
2.8%
Other values (5) 3915
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45365
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 7586
16.7%
0 7536
16.6%
9 5786
12.8%
( 3793
8.4%
, 3793
8.4%
3793
8.4%
] 3793
8.4%
2 2289
 
5.0%
1 1812
 
4.0%
6 1269
 
2.8%
Other values (5) 3915
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45365
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 7586
16.7%
0 7536
16.6%
9 5786
12.8%
( 3793
8.4%
, 3793
8.4%
3793
8.4%
] 3793
8.4%
2 2289
 
5.0%
1 1812
 
4.0%
6 1269
 
2.8%
Other values (5) 3915
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45365
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 7586
16.7%
0 7536
16.6%
9 5786
12.8%
( 3793
8.4%
, 3793
8.4%
3793
8.4%
] 3793
8.4%
2 2289
 
5.0%
1 1812
 
4.0%
6 1269
 
2.8%
Other values (5) 3915
8.6%

cluster_k_4
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size250.8 KiB
1
3891 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3891
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 3891
100.0%

Length

2025-06-18T10:03:01.024544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:03:01.058912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 3891
100.0%

Most occurring characters

ValueCountFrequency (%)
1 3891
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3891
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3891
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3891
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3891
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3891
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3891
100.0%

Interactions

2025-06-18T10:02:55.174701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:52.161129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:52.979612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:53.435683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:53.891672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:54.345544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:54.737248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:55.253560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:52.243843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:53.057741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:53.513845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:53.954163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:54.408889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:54.815365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:55.331675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:52.619593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:53.120220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:53.576340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:54.016659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:54.455763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:54.862236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:55.410642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:52.698318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:53.182716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:53.638830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:54.094710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:54.502560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:54.924727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:55.488768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:52.776509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:53.247560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:53.702183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:54.157274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:54.565125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:54.987232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:55.535628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:52.839004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:53.310023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:53.764760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:54.204135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:54.627619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:55.049680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:55.598053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:52.901470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:53.372538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:53.812285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:54.267431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:54.690112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:02:55.112208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-18T10:03:01.113914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
EstadoTipoSocietarioanio_preinscripcionantiguedadcant_Apoderadocant_antecedentescant_autenticadocant_noAutenticadocant_procesos_adjudicadocant_representantecant_sinMontoLimitecant_socioscant_suspensionesdcant_procesos_adjudicadodmonto_total_adjudicadodtotal_articulos_proveemonto_total_adjudicadoperiodo_preinscripcionprovinciatotal_articulos_provee
Estado1.0000.1260.1190.1360.0960.0000.1210.2400.0000.2630.0990.2160.0000.1080.1250.1430.0351.0000.0880.000
TipoSocietario0.1261.0000.0680.0640.0550.0000.1250.0000.0000.0000.1350.4720.0000.0000.1620.0500.0611.0000.0810.000
anio_preinscripcion0.1190.0681.0001.0000.0110.0000.0000.0000.0550.0000.0000.3420.1890.1250.0990.0790.0290.9990.3320.041
antiguedad0.1360.0641.0001.0000.0230.0770.0000.0000.3040.0000.0110.380-0.2920.1580.1130.0950.008-0.9760.1020.181
cant_Apoderado0.0960.0550.0110.0231.0000.0000.6920.8680.0000.4500.9370.6330.0000.0450.1050.0470.2911.0000.0000.015
cant_antecedentes0.0000.0000.0000.0770.0001.0001.000NaN-0.0700.0000.000NaN0.8340.1240.0000.000-0.088-0.0910.434-0.052
cant_autenticado0.1210.1250.0000.0000.6921.0001.0000.4750.0000.4270.6880.3061.0000.0000.0960.0510.0911.0000.0900.000
cant_noAutenticado0.2400.0000.0000.0000.868NaN0.4751.0000.0000.4480.8411.000NaN0.1320.0000.2810.5541.0000.0000.000
cant_procesos_adjudicado0.0000.0000.0550.3040.000-0.0700.0000.0001.0000.0000.0001.000-0.1790.2250.1820.0950.527-0.2840.0000.375
cant_representante0.2630.0000.0000.0000.4500.0000.4270.4480.0001.0000.8361.0000.0000.0000.5320.0000.0001.0000.3291.000
cant_sinMontoLimite0.0990.1350.0000.0110.9370.0000.6880.8410.0000.8361.0000.2940.0000.0350.1060.0370.2541.0000.0370.000
cant_socios0.2160.4720.3420.3800.633NaN0.3061.0001.0001.0000.2941.000NaN0.0001.0000.3361.0001.0001.0001.000
cant_suspensiones0.0000.0000.189-0.2920.0000.8341.000NaN-0.1790.0000.000NaN1.0000.3160.0000.000-0.2050.2560.0000.005
dcant_procesos_adjudicado0.1080.0000.1250.1580.0450.1240.0000.1320.2250.0000.0350.0000.3161.0000.2280.1650.0760.0700.0640.128
dmonto_total_adjudicado0.1250.1620.0990.1130.1050.0000.0960.0000.1820.5320.1061.0000.0000.2281.0000.0630.2360.0950.0510.091
dtotal_articulos_provee0.1430.0500.0790.0950.0470.0000.0510.2810.0950.0000.0370.3360.0000.1650.0631.0000.0311.0000.0540.217
monto_total_adjudicado0.0350.0610.0290.0080.291-0.0880.0910.5540.5270.0000.2541.000-0.2050.0760.2360.0311.0000.0040.0000.134
periodo_preinscripcion1.0001.0000.999-0.9761.000-0.0911.0001.000-0.2841.0001.0001.0000.2560.0700.0951.0000.0041.0000.918-0.187
provincia0.0880.0810.3320.1020.0000.4340.0900.0000.0000.3290.0371.0000.0000.0640.0510.0540.0000.9181.0000.000
total_articulos_provee0.0000.0000.0410.1810.015-0.0520.0000.0000.3751.0000.0001.0000.0050.1280.0910.2170.134-0.1870.0001.000

Missing values

2025-06-18T10:02:55.739478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-18T10:02:55.911321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-18T10:02:56.130053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CUITNombreFechaPreinscripcionEstadoTipoSocietarioperiodo_preinscripcionanio_preinscripcioncant_procesos_adjudicadomonto_total_adjudicadoantiguedadprovinciacant_socioscant_apercibimientoscant_suspensionescant_antecedentescant_Apoderadocant_representantecant_autenticadocant_noAutenticadocant_sinMontoLimitecant_MontoLimitetotal_articulos_proveedmonto_total_adjudicadodcant_procesos_adjudicadodtotal_articulos_proveecluster_k_4
027236909900EMR VENTAS & SERVICIOS04/10/2016InscriptoPersona Física201610201668.01.069618e+075.0ChubutNaNNaNNaNNaN1.0NaN1.0NaN1.0NaN68.0(9424898.401, 13557176.81](39.0, 1214.0](58.0, 97.6]1
820305924076Suministros EDA13/10/2016InscriptoPersona Física2016102016147.09.393091e+075.0Ciudad Autónoma de Buenos AiresNaNNaNNaNNaN2.0NaN1.01.02.0NaN107.0(89439449.702, 222964579.98](39.0, 1214.0](97.6, 161.0]1
920082883240SEGUMAX de HORACIO MIGUEL ESPOSITO18/10/2016Desactualizado Por Documentos VencidosPersona Física20161020162.01.216063e+065.0Ciudad Autónoma de Buenos AiresNaNNaNNaNNaN1.0NaN1.0NaN1.0NaN263.0(890758.9, 1302657.558](0.999, 2.0](161.0, 345.0]1
1320230506060sin datos15/08/2016InscriptoPersona Física201608201612.01.209260e+085.0Ciudad Autónoma de Buenos AiresNaNNaNNaNNaN1.0NaN1.0NaN1.0NaN5.0(89439449.702, 222964579.98](8.0, 12.0](4.0, 6.0]1
1620141208269sin datos23/09/2016InscriptoPersona Física20160920161.06.407980e+055.0Ciudad Autónoma de Buenos AiresNaNNaNNaNNaN1.0NaN1.0NaN1.0NaN16.0(599760.0, 890758.9](0.999, 2.0](15.0, 21.0]1
3020271472901CONTRACTUAL17/08/2016Desactualizado Por Documentos VencidosPersona Física20160820161.01.900260e+055.0Ciudad Autónoma de Buenos AiresNaNNaNNaNNaN1.0NaN1.0NaN1.0NaN10.0(104767.373, 224078.198](0.999, 2.0](8.0, 11.0]1
3220302787221sin datos30/07/2016Desactualizado Por Documentos VencidosPersona Física20160720163.03.289500e+045.0Ciudad Autónoma de Buenos AiresNaNNaNNaNNaN1.0NaN1.0NaN1.0NaN8.0(-0.001, 33011.111](2.0, 3.0](6.0, 8.0]1
5030546666707UNIVERSIDAD NACIONAL DE LA PLATA09/11/2016InscriptoOrganismo Publico201611201629.01.624050e+095.0Buenos AiresNaNNaNNaNNaN1.01.02.0NaN2.0NaN21.0(222964579.98, 46172150151.0](19.0, 39.0](15.0, 21.0]1
5620103522324sin datos21/10/2016InscriptoPersona Física20161020161.05.828571e+055.0MendozaNaNNaNNaNNaN1.0NaN1.0NaN1.0NaN8.0(377939.298, 599760.0](0.999, 2.0](6.0, 8.0]1
9920264053367SERVISUR21/10/2016InscriptoPersona Física2016102016134.08.692312e+075.0Buenos AiresNaNNaNNaNNaN1.0NaN1.0NaN1.0NaN659.0(46718747.516, 89439449.702](39.0, 1214.0](345.0, 6993.0]1
CUITNombreFechaPreinscripcionEstadoTipoSocietarioperiodo_preinscripcionanio_preinscripcioncant_procesos_adjudicadomonto_total_adjudicadoantiguedadprovinciacant_socioscant_apercibimientoscant_suspensionescant_antecedentescant_Apoderadocant_representantecant_autenticadocant_noAutenticadocant_sinMontoLimitecant_MontoLimitetotal_articulos_proveedmonto_total_adjudicadodcant_procesos_adjudicadodtotal_articulos_proveecluster_k_4
1005820385356243DIAZ JONATAN MACIEL13/09/2022InscriptoPersona Física20220920221.05.666667e+050.0ChubutNaNNaNNaNNaN1.0NaN1.0NaN1.0NaN1.0(377939.298, 599760.0](0.999, 2.0](0.999, 2.0]1
1005920322310944E.F.R.26/08/2022InscriptoPersona Física20220820221.04.047619e+050.0JujuyNaNNaNNaNNaN1.0NaN1.0NaN1.0NaN253.0(377939.298, 599760.0](0.999, 2.0](161.0, 345.0]1
1006220149549987TRANSPORTES ROBOL07/02/2017Desactualizado Por Mantencion FormularioPersona Física20170220171.05.261905e+064.0CorrientesNaNNaNNaNNaN2.0NaN2.0NaN2.0NaN4.0(4727330.113, 6702697.888](0.999, 2.0](3.0, 4.0]1
1006324925253304ALBERTO FREDY MARTIN15/02/2021InscriptoPersona Física20210220211.01.398857e+060.0Buenos AiresNaNNaNNaNNaN1.0NaN1.0NaN1.0NaN29.0(1302657.558, 1793326.755](0.999, 2.0](21.0, 29.0]1
1006520328156742M&M01/09/2022InscriptoPersona Física20220920221.00.000000e+000.0CorrientesNaNNaNNaNNaN1.0NaN1.0NaN1.0NaN1.0(-0.001, 33011.111](0.999, 2.0](0.999, 2.0]1
1006720171591563BIOTECNIKA16/06/2022InscriptoPersona Física20220620221.02.986090e+060.0Buenos AiresNaNNaNNaNNaN1.0NaN1.0NaN1.0NaN22.0(2483085.385, 3396600.0](0.999, 2.0](21.0, 29.0]1
1006820293290416ELIAS MARTIN SEGURA26/08/2022InscriptoPersona Física20220820221.01.873469e+050.0Ciudad Autónoma de Buenos AiresNaNNaNNaNNaN1.0NaN1.0NaN1.0NaN1.0(104767.373, 224078.198](0.999, 2.0](0.999, 2.0]1
1006920240423759FEDERICO MARTIN NUÑEZ26/08/2022InscriptoPersona Física20220820221.03.226531e+050.0Ciudad Autónoma de Buenos AiresNaNNaNNaNNaN1.0NaN1.0NaN1.0NaN1.0(224078.198, 377939.298](0.999, 2.0](0.999, 2.0]1
1007120287286687LAZARTE MARIO03/08/2022InscriptoPersona Física20220820221.05.792102e+060.0Buenos AiresNaNNaNNaNNaN1.0NaN1.0NaN1.0NaN1.0(4727330.113, 6702697.888](0.999, 2.0](0.999, 2.0]1
1007527331126530sin datos18/08/2022InscriptoPersona Física20220820221.09.367347e+040.0Ciudad Autónoma de Buenos AiresNaNNaNNaNNaN1.0NaN1.0NaN1.0NaN4.0(33011.111, 104767.373](0.999, 2.0](3.0, 4.0]1